boiwsa is an R package for the seasonal adjustment and forecasting of weekly time series data. It offers a user-friendly interface for computing seasonally adjusted estimates and includes diagnostic tools to assess the quality of the adjustments.
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wex is an R package for computing exact observation weights for the Kalman filter and smoother, following Koopman and Harvey (2003). The package provides tools for analyzing linear Gaussian state-space models by quantifying how individual observations contribute to filtered and smoothed state estimates. These weights are particularly useful in applications such as dynamic factor models, where they can be used to decompose latent factors into contributions from observed variables (see Example 2 on GitHub).
cforecast is an R package that provides tools for conducting scenario analysis in reduced-form vector autoregressive (VAR) models. It implements a Kalman filtering framework to generate forecasts under path restrictions on selected variables. The package enables decomposition of conditional forecasts into variable-specific contributions and extraction of observation weights. It also computes measures of overall and marginal variable importance to enhance the economic interpretation of forecast revisions. The framework is structurally agnostic and suited for policy analysis, stress testing, and macro-financial applications.
Ginker, T., Ilek, A., and Snir, A. (2024). Rigidity and Synchronization: Analyzing Online and Offline Price Dynamics.
A GitHub repo with a replication code for our method of computing the benchmark for the index of price synchronization under the null hypothesis of no coordination (i.e. independence) between the stores.
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